Uncategorized – Duke AI Health

Vanderbilt and Duke Awarded Moore Foundation Grant to Improve Oversight of AI Technology in Health Care Systems

Vanderbilt University Medical Center (VUMC) and Duke University School of Medicine were awarded a $1.25 million grant from the Gordon and Betty Moore Foundation for the project “Measuring Artificial Intelligence (AI) Maturity in Healthcare Organizations.” Working with the Coalition for Health AI (CHAI) and the University of Iowa, a team of experts will leverage the grant to develop a maturity model framework. The project leads are Peter Embí, MD, MS, and Laurie Novak, PhD, MHSA, from VUMC; and Michael Pencina, PhD, and Nicoleta Economou, PhD, from Duke. This framework will outline the essential capabilities that health systems must establish to ensure they are well-prepared for the trustworthy utilization of AI models.

Duke AI Health welcomes Ozanan R. Meireles, MD

The Duke Department of Surgery is pleased to announce the appointment of Ozanan R. Meireles, MD, as the department’s first Vice Chair for Innovation, effective Jan. 2, 2024.

Dr. Meireles is an internationally known authority in surgical applications of Artificial Intelligence (AI) and comes to us from Massachusetts General Hospital (MGH) where he specializes in minimally invasive surgery and runs the MGH Surgical AI and Innovation Lab (SAIIL). This lab boasts an impressive and longstanding close collaboration of more than eight years with MIT’s renowned Computer Science and Artificial Intelligence Lab (MIT-CSAIL), a partnership cultivated under the guidance of Professor Daniela Rus. Subsequently, he is bringing SAIIL to Duke as director of the lab. He will work within the School of Medicine as Surgical Director of Duke AI Health and advise on surgical AI applications emerging in the Health System. He will also serve as Vice Chair of Innovation for the Department of Surgery.

“We are delighted to welcome Dr. Meireles to Duke, where the intersection of medicine and data science serves as the cornerstone for innovation in health research and healthcare delivery,” says Michael Pencina, PhD, Chief Data Scientist for Duke Health, and Director of Duke AI Health. “His expertise in AI will play a vital role in advancing our data-driven collaborative initiatives, and we look forward to the groundbreaking discoveries and advancements that will result from his contributions.”

READ THE FULL ANNOUNCEMENT

Registration now open for the Electronic Health Record (EHR) Study Design Workshop

Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2023. The workshop will be offered in December as a virtual five-day class (December 4 – 8, 2023) that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies.

The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. The EHR-SDW will introduce the components of EHR data and introduce considerations for design of effective studies. In addition to didactic lectures, participants will get hands-on experience in working with publicly available tools to facilitate EHR studies (e.g., RxNorm, CCS codes, geocoding) as well as feedback on effective study designs that they will work on. The course will be conducted virtually via Zoom.

To register for the EHR-SDW, please visit https://events.duke.edu/ehr-sdw-2023
To request consideration for a scholarship, please visit https://duke.qualtrics.com/jfe/form/SV_a3Fs9TyyK82Bkr4

The deadline for registration is Tuesday, November 28, 2023.

Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2023

Duke AI Health is pleased to announce the Duke Electronic Health Records Study Design Workshop (EHR-SDW) 2023. The workshop will be offered December 4^th through 8^th as a virtual five-day class that provides foundational lectures and hands-on studios on the fundamentals of working with and designing EHR based studies. The EHR-SDW is targeted toward individuals interested in learning about how to work with and conduct studies using electronic health records (EHR) data. EHR data are a widely available form of real-world data that have become standard in studies ranging from clinical trials, comparative effectiveness, risk prediction, and population health. The EHR-SDW will introduce the components of EHR data and introduce considerations for design of effective studies. In addition to didactic lectures, participants will get hands-on experience in working with publicly available tools to facilitate EHR studies (e.g., RxNorm, CCS codes, geocoding) as well as feedback on effective study designs that they will work on. The course will be conducted virtually via Zoom. This workshop is offered through Duke AI Health’s Health Data Science (HDS) program and builds on the success of the Electronic Health Records Study Design Workshop held in December 2022 and highly successful Machine Learning Schools, with 12 events held since 2017. The Duke Machine Learning Schools have reached hundreds of participants from academia and industry and including international audiences at the SingHealth/Duke NUS Medical School and the Duke Kunshan University campus. Our 2022 Duke Machine Learning Summer School attracted 140 participants from around the world, representing 41 universities, institutes, and corporations.

Duke AI Health’s Nicoleta Economou talks guidelines & guardrails for responsible health AI development in AIMed “Champions” interview

Nicoleta Economou-Zavlanos, PhD, the director of Governance and Evaluation of health AI systems at Duke AI Health, was recently interviewed by AIMed’s Gemma Lovegrove for their AI Champions Interview Series, which highlights key thought leaders in the AI space. During the interview, Dr. Economou underscored the importance of incorporating fairness, transparency, and inclusivity throughout the entire process of health AI development, implementation, and monitoring.

CHI Conference on Artificial Intelligence and the Future of Digital Healthcare

The Connected Health Initiative (CHI) is hosting an in-person conference titled ‘Artificial Intelligence and the Future of Digital Healthcare at the Crossroads’ on September 26, 2023, at the National Press Club in Washington, D.C., from 12:30 PM to 5:35 PM EDT. The event will delve into the profound impact of AI systems on healthcare, offering potential for improved outcomes, cost savings, and a shift towards proactive disease prevention. Duke AI Health Director Michael Pencina, PhD, ABCDS Director Nicoleta Economou-Zavlanos, PhD, and AI Health Equity Scholar Michael Cary, PhD, RN, will be presenting the Algorithm-Based Clinical Decision Support (ABCDS) Oversight framework at the conference, touching upon the program’s design, implementation and strategies for bias mitigation and ensuring health equity. The CHI conference aims to foster a vital public dialogue on the state of health AI, proactive approaches by leading organizations to address AI efficacy, and the government’s role in managing AI’s risks and opportunities in healthcare.

Duke AI Health Director Michael Pencina Named Duke Health’s First Chief Data Scientist

Michael Pencina, PhD, vice dean for data science, professor of biostatistics and bioinformatics at Duke University School of Medicine, and director of Duke AI Health, has been named Duke Health’s first chief data scientist. Executive Vice President for Health Affairs and Dean Mary E. Klotman, MD, and Duke University Health System Chief Executive Officer Craig Albanese, MD, MBA, announced Pencina’s appointment. “In the current era of rapid expansion of AI and data science, we created this new role in recognition of the need for a well-articulated strategy for Duke Health that spans and connects both our academic and our clinical missions,” Klotman and Albanese said in their announcement.

Duke Researchers Develop Prediction Model to Identify Children With Complex Health Needs At Risk for Hospitalization

An important study led by Duke’s David Ming, MD, and AI Health’s Benjamin Goldstein, PhD, and Nicoleta Economou, PhD, on the use of predictive modeling to identify children with complex health needs who are at high risk for hospitalization, was recently published in Hospital Pediatrics, the official journal of the American, Academy of Pediatrics. The study analyzed data from electronic health records and found that certain demographic, clinical, and health service use factors were associated with a higher risk of future hospitalization. The authors, including Duke’s Richard Chung, MD, and Ursula Rogers, BS, suggest that the use of predictive modeling can help identify children with complex health needs who may benefit from targeted interventions to prevent hospitalizations and improve outcomes. The study is accompanied by a commentary by University of Wisconsin Neil Munjal, MD, MS, titled ‘Machine Learning: Predicting Future Clinical Deterioration in Hospitalized Pediatric Patients,’ which describes the Duke researchers’ machine learning approach as “thought-provoking.”

Coalition for Health AI Unveils Blueprint for Trustworthy AI in Healthcare

The Coalition for Health AI (CHAI) released its highly anticipated “Blueprint for Trustworthy AI Implementation Guidance and Assurance for Healthcare” (Blueprint). The Blueprint addresses the quickly evolving landscape of health AI tools by outlining specific recommendations to increase trustworthiness within the healthcare community, ensure high-quality care, and meet healthcare needs. The 24-page guide reflects a unified effort among subject matter experts from leading academic medical centers and the healthcare, technology, and other industry sectors, who collaborated under the observation of several federal agencies over the past year.

Algorithms to Assess Stroke Risk are Markedly Worse for Black Americans

Current medical standards for accessing stroke risk perform worse for Black Americans than they do for white Americans, potentially creating a self-perpetuating driver of health inequities. A study, led by Duke Health researchers and appearing online Jan. 24 in the Journal of the American Medical Association, evaluated various existing algorithms and two methods of artificial intelligence assessment that are aimed at predicting a person’s risk of stroke within the next 10 years. The study found that all algorithms were worse at stratifying the risk for people who are Black than people who are white, regardless of the person’s gender. The implications are at the individual and population levels: people at high risk of stroke might not receive treatment, and those at low or no risk are unnecessarily treated.